Project

0.1 Importing the dataset

df <- read.csv2('https://raw.githubusercontent.com/mcasky16/RR_Final_Project/main/student_feedback.csv', sep = ',')

COLUMN NAMES names(df) ## [1] “X”
## [2] “Student.ID”
## [3] “Well.versed.with.the.subject”
## [4] “Explains.concepts.in.an.understandable.way”
## [5] “Use.of.presentations”
## [6] “Degree.of.difficulty.of.assignments”
## [7] “Solves.doubts.willingly”
## [8] “Structuring.of.the.course”
## [9] “Provides.support.for.students.going.above.and.beyond” ## [10] “Course.recommendation.based.on.relevance”

POTENTIAL QUESTIONS TO BE ANSWERED How is “course recommendation based on relevance distributed” as density plot?

Average use of presentation

CORRELATION between solves doubt AND explain concept in an understandable way

Bar chart student well versed with the topic

on the scale to one to 10 how much difficult assignment is 6- scale and 7-scale

Bar chart rating for teacher that Solves doubt willingly

REMOVING MISSING VALUES TO CLEAN THE DATA AND AVOID ERRORS df_nona implies df with no null values

df_nona = df%>%drop_na()

How is “course recommendation based on relevance distributed” as density plot?

## Warning: Ignoring unknown parameters: bins

## Average use of presentation

df_nona %>%
summarize(average_use_ofpresentation= round(mean(Use.of.presentations)))

##   average_use_ofpresentation
## 1                          6

0.2 average_use_ofpresentation

0.3 1 6

on the scale of 1-10 average ratimg that student feel that they will use presentation for topic is 6

on the scale to one to 10 ho much difficult assignment is 6- scale and 7-scale

df_nona %>%
  filter(Degree.of.difficulty.of.assignments %in% c('6', '7'))%>%
  ggplot() + 
  geom_density(aes(x = Degree.of.difficulty.of.assignments , fill = Degree.of.difficulty.of.assignments , alpha = 0.1))+
  labs(title = 'Degree.of.difficulty.of.assignments',
       subtitle= 'Mumbai student survey' , x= 'Degree.of.difficulty.of.assignments',y= 'Frequency') +
  ggthemes::theme_stata()

0.4 Bar chart student well versed with the topic

df_nona %>%
  group_by(Well.versed.with.the.subject)%>%
  count(sort =TRUE) %>%
  head(10)%>%
  ggplot() +
  geom_col(aes(x= reorder(Well.versed.with.the.subject , n) , y = n), fill = "Pink")  + 
  geom_label(aes(x= reorder(Well.versed.with.the.subject , n) , y = n , label=n))+
  coord_flip() +
  labs(title = 'no. of student who are well versed ',
        x= 'scale',y= 'Frequency')+
  ggthemes::theme_economist_white()-> scale.... 
plotly:: ggplotly(scale....)

0.5 Bar chart rating for teacher that Solves doubt willingly

df_nona %>%
  group_by(Solves.doubts.willingly)%>%
  count(sort =TRUE)%>%
  ggplot() +
  geom_col(aes(x= reorder(Solves.doubts.willingly, n) , y = n), fill = "purple" )  + 
  geom_label(aes(x= reorder(Solves.doubts.willingly , n) , y = n , label=n))+
 
  ggthemes::theme_gdocs()->gg....Solves.doubts.willingly
plotly:: ggplotly(gg....Solves.doubts.willingly)

0.6 CORRELATION BETWEEN solves doubt AND explain concept in an understandable way

df_nona%>%
  select(c('Solves.doubts.willingly' ,'Explains.concepts.in.an.understandable.way')) %>%
  cor()

##                                            Solves.doubts.willingly
## Solves.doubts.willingly                                 1.00000000
## Explains.concepts.in.an.understandable.way             -0.02583881
##                                            Explains.concepts.in.an.understandable.way
## Solves.doubts.willingly                                                   -0.02583881
## Explains.concepts.in.an.understandable.way                                 1.00000000

correlation less than 0 indicate a negative correlation in our case it is -0.0258

0.7 SCATTER PLOT

Since the points or values are range in absolute integer the scatter plot is not showing good result

df_nona%>%
  select(c('Solves.doubts.willingly' ,'Explains.concepts.in.an.understandable.way')) %>%
  ggplot(aes(x= Solves.doubts.willingly , y = Explains.concepts.in.an.understandable.way))+
  geom_point(color = "#6c094f")+
  geom_smooth(method = lm)+
  labs(title = "correlation Scatter Plot" )